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Doctoral Thesis
DOI
10.11606/T.45.2011.tde-17112011-160718
Document
Author
Full name
Kelly Rosa Braghetto
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2011
Supervisor
Committee
Ferreira, João Eduardo (President)
Fernandes, Paulo Henrique Lemelle
Lejbman, Alfredo Goldman Vel
Lima, Ricardo Massa Ferreira
Mattoso, Marta Lima de Queirós
Title in Portuguese
Técnicas de modelagem para a análise de desempenho de processos de negócio
Keywords in Portuguese
Análise de Desempenho
Modelagem
Processos de Negócio
Redes de Autômatos Estocásticos
Abstract in Portuguese
As recentes pesquisas na área de Gestão de Processos de Negócio (GPN) vêm contribuindo para aumentar a eficiência nas organizações. A GPN pode ser compreendida como o conjunto de métodos, técnicas e ferramentas computacionais desenvolvidas para amparar os processos de negócios. Tipicamente, a GPN é fundamentada por modelos de processos. Esses modelos, além de permitirem a automação da configuração e execução, aumentam a capacidade de análise dos processos de negócio. Apesar de auxiliar os especialistas de negócio nas diferentes fases envolvidas no ciclo de vida de um processo de negócio (projeto, configuração, implantação/execução e a análise), os modelos definidos em linguagens específicas de domínio, como a BPMN (Business Process Model and Notation), não são os mais apropriados para amparar a fase de análise. De formal geral, esses modelos não possuem uma semântica operacional formalmente definida (o que limita o seu uso para a verificação e validação dos processos) e nem mecanismos para quantificar o comportamento modelado (o que impossibilita a análise de desempenho). Neste trabalho de doutorado, nós desenvolvemos um arcabouço que ampara e automatiza os principais passos envolvidos na análise de desempenho de processos de negócio via modelagem analítica. Nós estudamos a viabilidade da aplicação de três formalismos Markovianos na modelagem de processos de negócio: as Redes de Petri Estocásticas, as Álgebras de Processo Estocásticas e as Redes de Autômatos Estocásticos (SAN, do inglês Stochastic Automata Networks). Escolhemos SAN como formalismo base para o método proposto neste trabalho. Nosso arcabouço é constituído por: (i) uma notação para enriquecer modelos de processos de negócio descritos em BPMN com informações sobre o seu gerenciamento de recursos, e (ii) um algoritmo que faz a conversão automática desses modelos não-formais de processos para modelos estocásticos em SAN. Com isso, somos capazes de capturar o impacto causado pela contenção de recursos no desempenho de um processo de negócio. A partir de um modelo em SAN gerado com o nosso arcabouço, podemos predizer variados índices de desempenho que são boas aproximações para o desempenho esperado do processo de negócio no mundo real.
Title in English
Modeling techniques for business process performance analysis
Keywords in English
Business Processes
Modeling
Performance Analysis
Stochastic Automata Networks
Abstract in English
Recent results in the research field of Business Process Management (BPM) are contributing to improve efficiency in organizations. BPM can be seen as a set of methods, techniques and tools developed to support business processes in their different requirements. Usually, the BPM techniques are based on a process model. In addition to enabling automated process configuration and execution, these models also increase the analizability of business processes. Despite being able to support business specialists in different phases of the life cycle of a business process (design, configuration, execution, and analysis), the models created in domain-specific languages, such as BPMN (Business Process Model and Notation), are not the most appropriated ones to support the analysis phase. Generally, these models have neither a formally defined operational semantics (which hinders their use for verification and validation), nor mechanisms to quantify the modeled behavior (which hinders their use for performance analysis). In this PhD research, we developed a framework to support and to automatize the main steps involved in the analytical modeling of business processes aiming performance evaluation. We studied the viability of applying three Markovian formalisms in business process modeling: Stochastic Petri Nets, Stochastic Process Algebras and Stochastic Automata Networks (SAN). We have chosen SAN to support the method proposed in this work. Our framework is composed of: (i) a notation to enrich BPMN business process models with information concerning the associated resource management and (ii) an algorithm that automatically converts these non-formal business process models in SAN stochastic models. With this, we are able to capture the impact caused by resource contention in the performance of a business process. From a model generated through our framework, we are able to extract varied performance indices that are good approximations for the expected process performance in the real world.
 
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tese_krbraghetto.pdf (1.97 Mbytes)
Publishing Date
2012-04-12
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • BRAGHETTO, K. R., FERREIRA, J. E., e VINCENT, J. Performance evaluation of resource-aware busness processes using stochastic automata networks. International Journal of Innovative Computing, Information & Control, 2012, vol. v.8, p. 5295-5316.
  • BRAGHETTO, Kelly Rosa, FERREIRA, João Eduardo, and VINCENT, Jean-Marc. Performance Evaluation of Resource-Aware Business Processes Using Stochastic Automata Networks. International Journal of Innovative Computing, Information and Control, 2012, vol. 8, n. 7, p. 1-22.
  • BRAGHETTO, K. R., FERREIRA, J. E., and VINCENT, J. Performance Analysis Modeling Applied to Business Processes. In Symposium On Theory of Modeling and Simulation - DEVS Integrative M&S Symposium - DEVS'10, Orlando - FL, 2010. Symposium On Theory of Modeling and Simulation - DEVS Integrative M&S Symposium., 2010.
  • BRAGHETTO, Kelly Rosa, FERREIRA, João Eduardo, and VINCENT, Jean-Marc. Performance analysis modeling applied to business processes [doi:10.1145/1878537.1878665]. In Spring Simulation Multiconference (SpringSim'10) [online], Orlando, FL, USA, 2010. San Diego, CA, USA : Society for Computer Simulation International, 2010. p. 1-8. ISBN 9781450300.
  • BRAGHETTO, Kelly Rosa, FERREIRA, João Eduardo, and VINCENT, Jean-Marc. Performance Evaluation of Business Processes through a Formal Transformation to SAN [doi:10.1007/978-3-642-24749-1_5]. In Lecture Notes in Computer Science [online], 8, Borrowdale, Reino Unido, 2011. Heidelberg : Springer, 2011. vol. 6977, p. 42-56. ISBN 9783642247.
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